Computer methods and programs in biomedicine
Mar 20, 2025
BACKGROUND AND OBJECTIVE: In recent years, deep U-shaped network architectures have been widely applied to medical image segmentation tasks, achieving notable successes. However, the inherent limitation of this architecture is that multiple down-samp...
Medical imaging plays a vital role as an auxiliary tool in clinical diagnosis and treatment, with segmentation serving as a crucial foundational process in medical image analysis. Nonetheless, challenges such as class imbalance and indistinct boundar...
Label scarcity, class imbalance and data uncertainty are three primary challenges that are commonly encountered in the semi-supervised medical image segmentation. In this work, we focus on the data uncertainty issue that is overlooked by previous lit...
Evidence-based medicine is the preferred procedure among clinicians for treating patients. Content-based medical image retrieval (CBMIR) is widely used to extract evidence from a large archive of medical images. Developing effective CBMIR systems for...
Computer methods and programs in biomedicine
Mar 13, 2025
BACKGROUND AND OBJECTIVE: Hierarchical neural networks are pivotal in medical imaging for multi-scale representation, aiding in tasks such as object detection and segmentation. However, their effectiveness is often limited by the loss of intra-scale ...
Classifying medical images is essential in computer-aided diagnosis (CAD). Although the recent success of deep learning in the classification tasks has proven advantages over the traditional feature extraction techniques, it remains challenging due t...
Journal of nuclear medicine technology
Mar 5, 2025
The recent emergence of text-to-image generative artificial intelligence (AI) diffusion models such as DALL-E, Firefly, Stable Diffusion, and Midjourney has been touted with popular hype about the transformative potential in health care. This hype-dr...
Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emi...
Machine learning has emerged as a crucial tool for medical image analysis, largely due to recent developments in deep artificial neural networks addressing numerous, diverse clinical problems. As with any conceptual tool, the effective use of machine...